Learning model, signal processor, flying object, and program

ABSTRACT

The purpose of the present invention is to provide a learning model, a signal processor, a flying object, and a program that enable appropriate observation of the situation of an observed object or the environment around the observed object. The learning model is learned by using teaching data with a first received signal as input, the first received signal being based on a reflected electromagnetic wave that is an electromagnetic wave emitted to a first target area and then reflected, and with first meta-information as output, the first meta-information corresponding to the first received signal and having a predetermined item, so as to input a second received signal based on a reflected electromagnetic wave that is an electromagnetic wave emitted to a second target area and then reflected, and to output second meta-information corresponding to the second received signal and having a predetermined item.

TECHNICAL FIELD

The present invention relates to a learning model, a signal processor, aflying object, and a program.

BACKGROUND ART

Observation of the state of the earth's surface, both on the ground andat sea, is widely conducted using flying objects such as satellites,aircrafts, and drone devices. The observation methods by satellitesinclude those with the acquisition of optical images, with theacquisition of radar images obtained by using the synthetic apertureradar (SAR) technique, so-called SAR images, or with the acquisition ofboth optical and SAR images combined with each other. Patent Document 1discloses a geophysical information deciphering image generation methodfor generating a synthetic image in which a radar image such as a SARimage and an optical image are combined in such a way that geographicalobjects can be easily distinguished.

CITATION LIST Patent Document

-   Patent Document 1: Japanese Patent Application Laid-Open No.    2009-047516

SUMMARY Technical Problem

A SAR images is generated on the basis of a signal (hereinafter,referred to as “received signal”) corresponding to an electromagneticwave reflected from an observed object when a microwave (electromagneticwave) is emitted from a satellite equipped with a radar device to theobserved object. The SAR image is generated by applying a predeterminedcompression process to the received signal.

In the compression process of SAR image generation, the received signalis filtered in the frequency domain to remove a part of the receivedsignal data. Filtering reduces the amount of data in the received signalto be compressed, thus reducing the computational burden of thecompression process. On the other hand, the removal in the receivedsignal by filtering may cause missing information detectable from thereceived signal or false detection of information. Missing informationor false detection of information affects the precision or accuracy ofthe observation of the situation of the observed object or of theenvironment around the observed object.

Therefore, it is an object of the present invention to provide alearning model, a signal processor, a flying object, and a program,which enable the observation of a situation of an observed object or ofan environment around the observed object with high precision or highaccuracy.

Solution to Problem

A learning model according to an aspect of the present invention islearned by using teaching data with a first received signal as input,the first received signal being based on a reflected electromagneticwave that is an electromagnetic wave emitted to a first target area andthen reflected, and with first meta-information as output, the firstmeta-information corresponding to the first received signal and having apredetermined item, so as to input a second received signal based on areflected electromagnetic wave that is an electromagnetic wave emittedto a second target area and then reflected, and to output secondmeta-information corresponding to the second received signal and havinga predetermined item.

According to this aspect, the learning model is learned to outputmeta-information corresponding to the received signal for an input ofthe received signal, and operates. The use of this learning modelenables, for example, acquisition of meta-information having an item“the total number of moving objects or constructions in the target area”based on the received signal. Since the meta-information is able to beacquired from the received signal without, for example, a SAR image, theinformation contained in the received signal will not be missing.Therefore, the meta-information indicating the situation of the observedobject is able to be observed with high precision or high accuracy.

In the above aspect, the learning model may be learned by using teachingdata with the first received signal and a first generated signalgenerated based on the first received signal as input and with the firstmeta-information as output, so as to input the second received signaland a second generated signal generated based on the second receivedsignal and to output the second meta-information.

According to this aspect, the teaching data in the learning modelfurther includes the first generated signal that has been generated onthe basis of the first received signal. The first generated signal is,for example, a signal (SAR signal) for generating a SAR image based onthe first received signal. In the case where an input further includesdata generated on the basis of the received signal like the SAR signaland the meta-information of an object or the like is able to be observedmore appropriately with the SAR signal by using the learning model withthe meta-information as output, the situation of the observed object isable to be observed with high precision or high accuracy.

In the above aspect, the learning model may be learned by using teachingdata with the first received signal and information indicating anenvironment in the first target area as input and with the firstmeta-information as output, so as to input the second received signaland information indicating an environment in the second target area andto output the second meta-information.

According to this aspect, the teaching data in the learning modelfurther includes information indicating the environment in the firsttarget area. The information indicating the environment in the firsttarget area is, for example, weather conditions such as weather in thefirst target area, or environmental conditions caused by human factorssuch as smoke. By using the input that includes the informationindicating the environment in the second target area in the output ofthe second meta-information, the second meta-information is able to beobserved with high precision or high accuracy.

In the above aspect, the learning model may be learned by using theteaching data with the first meta-information containing the informationindicating the environment in the first target area as output, so as tooutput the second meta-information containing the information indicatingthe environment in the second target area.

The use of the learning model according to this aspect enablesacquisition of information indicating the environment in the secondtarget area, which is the environment around the observed object.Therefore, the environment around the observed object is able to beobserved with high precision or high accuracy.

In another aspect, the signal processor includes a storage unit thatstores the learning model according to the above aspect, a signalacquisition unit that acquires the second received signal, and anestimation unit that inputs the second received signal to the learningmodel and then estimates the second meta-information.

According to this aspect, the use of a signal processor alone enablessignal acquisition and estimation of the second meta-information withthe learning model. Thereby, for example, even in an environment withcertain restrictions on communication with the outside, such as, forexample, the space above the earth, meta-information is able to beestimated using the learning model, and the signal processor is able toobserve the situation of the observed object with high precision or highaccuracy.

In the above aspect, the signal processor may include: the estimationunit that inputs the second received signal at time 1 and the secondreceived signal at time 2 to the learning model according to the aboveaspect and then estimates the second meta-information at the time 1corresponding to the second received signal at the time 1 and the secondmeta-information at the time 2 corresponding to the second receivedsignal at the time 2; and a change determination unit that determines achange in the second target area on the basis of the secondmeta-information at the time 1 and the second meta-information at thetime 2.

According to this aspect, in the case where the second meta-informationin the second target area at the time 1 has changed at the time 2, thepresence or absence of the change is detectable. Based on themeta-information, the signal processor is able to determine the changein the second target area while observing the situation of the observedobject or the environment around the observed object with high precisionor high accuracy.

In the above aspect, the signal processor may further include a changeinformation output unit that outputs change information indicating thechange in the case where the determined change satisfies a predeterminedcondition.

According to this aspect, the signal processor outputs the contents ofthe change as change information in addition to the presence or absenceof the change in the case where the change satisfies the predeterminedcondition. This enables an external device to acquire the details of thechange. With the output of the change information, as necessary, fromthe signal processor based on the conditions, the amount ofcommunication between the signal processor and the outside and the powerconsumption required for communication in the signal processor are ableto be reduced.

In another aspect, a flying object includes a storage unit that storesthe learning model according to the above aspect, a signal acquisitionunit that acquires the second received signal, an estimation unit thatinputs the second received signal to the learning model and thenestimates the second meta-information, and a signal output unit thatoutputs an output signal based on the second meta-information to theoutside.

According to this aspect, the flying object is able to estimate thesecond meta-information using a learning model by itself. Thereby, theflying object, which is placed in an environment with certainrestrictions on communication with the outside, is able to estimate thesecond meta-information without communication with the outside. Thisreduces the amount of communication of the flying object and the powerconsumption required for the communication. Moreover, based on thesecond meta-information, the flying object is able to output the outputinformation including, for example, the second meta-information itselfor the information indicating the change of the second meta-informationto the outside. This enables transmission of the second meta-informationequivalent to the meta-information observed on the basis of the SAR datato the outside without transmitting large capacity data such as SAR datato the outside. This enables a reduction in the amount of communicationbetween the flying object and the outside and the power consumptionrequired for communication by the flying object.

In another aspect, the program may cause the computer to perform asignal acquisition process for acquiring the second received signal thatis input to the storage unit that stores the learning model according tothe above aspect, and an estimation process for inputting the secondreceived signal into the learning model and then estimating the secondmeta-information. This enables the computer to observe meta-informationindicating the situation of the observed object with high precision orhigh accuracy.

In the above aspect, the program may cause the computer to furtherperform a signal output process for outputting the output signal basedon the second meta-information to the outside. This enables, forexample, a flying object having a computer, in which the program isrecorded, to reduce the amount of communication between the flyingobject and the outside and the power consumption required forcommunication by the flying object.

Advantageous Effects of Invention

The present invention provides a learning model, a signal processor, aflying object, and a program that enable appropriate observation of thesituation of an observed object or the environment around the observedobject.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of an observation system according to thepresent embodiment.

FIG. 2 is a diagram for describing a learning model according to thepresent embodiment.

FIG. 3 is a diagram for describing learning of the learning modelaccording to the present embodiment.

FIG. 4 is a diagram for describing an example of information used forlearning the learning model according to the present embodiment.

FIG. 5 is a diagram for describing an example of information used forlearning of the learning model according to the present embodiment.

FIG. 6 is a diagram for describing correspondence of teaching data usedfor learning of the learning model according to the present embodiment.

FIG. 7 is a flowchart for describing a process in a flying objectaccording to the present embodiment.

FIG. 8 is a diagram for describing estimation of meta-information by asignal processor according to the present embodiment.

FIG. 9 is a diagram for describing an example of the meta-informationestimated by the signal processor according to the present embodiment.

FIG. 10 is a diagram for describing determination of a change in themeta-information by the signal processor according to the presentembodiment.

FIG. 11 is a flowchart for describing the processing of determining thechange by the signal processor according to the present embodiment.

FIG. 12 is a diagram for describing another aspect of learning andestimation of a learning model.

FIG. 13 is a block diagram illustrating another aspect of theobservation system.

DESCRIPTION OF EMBODIMENTS

With reference to the accompanying drawings, preferred embodiments ofthe present invention are described. In the drawings, those with thesame reference numerals have the same or similar configurations.

FIG. 1 illustrates a block diagram of an observation system 10 accordingto the present embodiment. The observation system 10 includes a flyingobject 100 and an observation device 200. The flying object 100 isplaced in a space above the earth, and the observation device 200 isplaced on the earth. In this embodiment, the flying object 100 observesa target area D on the surface of the earth by radar, and an observationsignal O processed by the flying object 100 is transmitted to theobservation device 200. The observation signal O is, for example, areceived signal acquired by the flying object 100 or meta-informationcorresponding to the received signal, described later.

The flying object 100 includes a communication antenna 101, a radardevice 102, and a signal processor 103. The flying object 100, which isa satellite capable of acquiring and processing received signals, isplaced in outer space and orbits around the earth. The flying object 100may be a geostationary satellite. The flying object 100 need only be anaircraft, a helicopter, a drone device, or any other device that is ableto be positioned above the earth.

The communication antenna 101 is an antenna for the flying object 100 tocommunicate with external devices provided on the earth or in outerspace.

The radar device 102 irradiates a target area D on the earth's surfacewith, for example, an electromagnetic wave (EM1), which is a microwave,and acquires a reflected electromagnetic wave EM2 that is anelectromagnetic wave EM1 reflected by an observed object in the targetarea D. The radar device 102 is, for example, a synthetic aperture radar(SAR). The reflected electromagnetic wave EM2 is processed and recordedby the radar device 102 as a received signal (RAW data) based onelectromagnetic wave fluctuations able to be handled by the flyingobject 100. The received signal is recorded, for example, as a signalintensity for each predetermined coordinate of the target area D. Theradar device 102 transmits the observation signal O to the observationdevice 200 through the communication antenna 101.

The radar device 102 includes a processor for controlling the process ofacquiring received signals and a storage device in which programsnecessary for the control are stored.

The signal processor 103 is an information processor that processes thereceived signals acquired by the radar device 102. The signal processor103 is a computer that has a memory or other storage areas to perform apredetermined process by the processor executing a program stored in thestorage area.

The signal processor 103 has a storage unit 104 and a control unit 105.The storage unit 104 is, for example, a semiconductor memory such as RAMor an optical disk. The storage unit 104 stores various information usedfor processing in the signal processor 103.

The storage unit 104 stores a learning model 1041. The learning model1041 is a program that has been learned to take a received signal asinput and to output the meta-information corresponding to the receivedsignal. The details of the meta-information and the learning model 1041are described later.

The control unit 105 performs signal processing in the signal processor103. The control unit 105 also controls the transmission of processingresults of received signals through the flying object 100. The controlunit 105 has a signal acquisition unit 1051, an estimation unit 1052, asignal output unit 1053, a change determination unit 1054, and a changeinformation output unit 1055.

The signal acquisition unit 1051 acquires a received signal from theradar device 102.

The estimation unit 1052 inputs the received signal acquired by thesignal acquisition unit 1051 to the learning model 1041 to acquiremeta-information corresponding to the received signal.

The signal output unit 1053 outputs the meta-information acquired by theestimation unit 1052 as an observation signal O to the observationdevice 200 through the communication antenna 101. The signal output unit1053 may also output the received signal corresponding to themeta-information as the observation signal O together with themeta-information.

The change determination unit 1054 determines a change in a situation ofan observed object in the target area D or a change in an environmentaround the observed object, on the basis of a plurality of pieces ofmeta-information corresponding to received signals acquired from thetarget area D at different times.

The change information outputting unit 1055 outputs change informationindicating a change determined by the change determination unit 1054, inthe case where the change satisfies a predetermined condition. Thechange information is, for example, information from which the changedmeta-information is extracted or information indicating the range of anarea in the target area D where a change has occurred, out of themeta-information. The processes by the change determination unit 1054and the change information output unit 1055 are described later.

The observation device 200 transmits a control signal to the flyingobject 100 to control the observation of the target area D by the flyingobject 100 and acquires an observation signal O from the flying object100. The observation device 200 has a communication unit 201 includingan antenna and a control unit that controls communication by theantenna. Information is transmitted to and received from the flyingobject 100 through the communication unit 201.

A signal processing unit 202 processes the observation signal O receivedfrom the flying object 100. The signal processing unit 202 performsprocessing of visualizing, for example, the observation result in thetarget area D by means of images, on the basis of the observation signalO acquired from the flying object 100.

Referring to FIGS. 2 to 6 , the learning of the learning model 1041according to the present embodiment is described.

FIG. 2 is a diagram for schematically describing the learning andestimation of the learning model 1041. The learning model 1041 islearned using learning data LD as teaching data. The learning data LDcontains a pair of a received signal R0 (first received signal) that hasbeen acquired by irradiating a certain target area (first target area)with an electromagnetic wave and meta-information MD0 (firstmeta-information) corresponding to the received signal R0. The learningmodel 1041 is learned with the received signal R0 as input and themeta-information MD0 as output.

For the correspondence of the meta-information MD0 to the receivedsignal R0, the received signal R0 is converted to SAR data. Only after apredetermined conversion process, the received signal R0 is able to beinformation that the user of the observation device 200 is able tounderstand. The conversion process of SAR data is performed, forexample, by the observation device 200. The user is able to understandthe observation result based on the received signal R0 through theanalysis and visualization processes based on the SAR data.

The SAR data has multiple levels depending on the contents of theconversion of the received signal R0. For example, there is a firstlevel of SAR data obtained by performing range compression andsingle-look azimuth compression on the received signal R0, as SAR data.The first-level SAR data is complex number information and includes theamplitude information and the phase information of the reflectedelectromagnetic wave EM2 in the target area D. By visualizing the firstlevel of SAR data as a SAR image, the user is able to understand thecontents of the received signal R0.

Another SAR data is the second level of SAR data, which is obtained byperforming range compression and multi-look azimuth compression on thereceived signal R0. The second level of SAR data enables visualizationof the received signal R0 with geometrically corrected SAR images.

Still another SAR data is the third level of SAR data, which is obtainedby performing range compression, single-look azimuth compression, andortho correction on the received signal R0. Applying the orthocorrection enables acquisition of a SAR image that is able to besuperimposed on an optical image.

As described above, the user is able to understand the observationresult in the target area D by converting the received signal R0 to SARdata and visualizing the SAR data as the SAR image. The user is able toassociate meta-information MD0 with the received signal R0 asinformation indicating the meaning of the observation result.Alternatively, a computer may be used to associate the meta-informationwith the received signal after associating the meta-information with theSAR image by using a learning model that associates the SAR image withthe meta-information.

The meta-information MO is, for example, information about a ship andoceanographic conditions in the case of an observation of the ship atsea. Specifically, information corresponding to the items of position,total length, and type of the ship is acquired from the SAR image, asinformation of the ship. In addition, the ship's speed is able to beacquired by using phase information based on the SAR data with complexcomponents. Furthermore, the wind direction and the wind speed at sea isable to be acquired on the basis of the SAR data, as information onoceanographic conditions. The wind direction and wind speed at sea areestimated on the basis of the backscattering coefficient of the SAR dataand the correlation calculated based on the actually-measured winddirection and wind speed data. The backscattering coefficient is acoefficient based on the intensity of the electromagnetic wave thatreturns in the irradiation direction out of irradiated electromagneticwaves scattered on the surface of the target area.

Additional information other than SAR data may be used to associatemeta-information MD0 with the received signal R0. For example, themeta-information MD0 about a ship may include AIS information, which isthe information such as position, ship name, ship registry, and totallength, acquired from an automatic identification system (AIS). Themeta-information MD0 may be generated on the basis of the AISinformation obtained at the time when the received signal R0 isacquired. As for information on the oceanographic conditions, themeta-information MD0 may include the wind direction, wind speed, andweather acquired from buoys located in offshore areas.

Meta-information MD0 has various items depending on the observed target.Meta-information for a land-based moving object, such as an automobileand an animal, includes information on the movement locus of the movingobject. This information is acquired from a change in SAR data due to achange in interference of the reflected electromagnetic wave caused bythe moving object. In this case, the meta-information may include themovement locus based on a GPS device attached to the moving object.

In the case where an inundated area at the time of a disaster is theobserved target, the range of the inundated area may be acquired as alow brightness area in the SAR image as meta-information. In this case,the meta-information may include the range of the inundated area basedon an optical image acquired by an aircraft or a satellite.

The meta-information used for crop management may be the degree of cropgrowth as meta-information. The degree of crop growth is estimated onthe basis of the backscattering coefficient of the SAR data and acorrelation calculated based on the actually-observed degree of cropgrowth. In this case, the meta-information may include the spectralreflectance characteristics from optical measurements and the height ofthe crop actually measured.

In the detection of buildings, information on a new building may be usedas meta-information. The information on the new building is estimated onthe basis of the backscattering coefficient of the SAR data and thecorrelation calculated based on the information on the new buildingactually observed. In this case, the information on the new building maybe acquired from the building information detected from optical imagesor from map information. In addition, information on the building typemay be acquired on the basis of a map or the like, and may be used asmeta-information.

Simulation of electromagnetic waves may also be used to associatemeta-information with received signals. In the simulation model,irradiation and reflection of electromagnetic waves are able to besimulated to generate received signals and SAR data. In this case,meta-information such as the conditions in the simulation model such as,for example, the number of ships, the shapes of ships, and thetrajectory of the position of a moving object is meta-information to beassociated with the generated received signals and SAR data.

Upon the input of a received signal R1 (second received signal) acquiredby the flying object 100 based on the electromagnetic wave applied bythe flying object 100 to another target area (second target area) intothe learned learning model 1041, the learning model 1041 outputsmeta-information MD1 (second meta-information) corresponding to thereceived signal R1.

In addition to the received signal R1, the learning model 1041 mayreceive additional information obtained when the received signal R1 isacquired and then output the meta-information MD1. For example, in theabove example of a ship, AIS information and information from buoys atsea may be additional information. In this specification, it is assumedthat the learning model 1041 has been learned using meta-information MD0based on the SAR data and on the additional information including theAIS information and the information from the buoys at sea. In this case,either the AIS information or the information from the buoys at sea isinput to the learning model 1041 as additional information whenestimating the meta-information MD1 using the learning model 1041. Theadditional information may be added to the SAR data in the input to thelearning model 1041, so that the meta-information is observed withhigher precision or higher accuracy.

Referring to FIGS. 3 to 6 , the relationship between the received signalR0 and the meta-information MD0 is described. This embodiment isdescribed by giving an example of a learning model that enablesestimation of ships at sea and the oceanographic conditions, namely, theenvironment around the ships.

In the example illustrated in FIG. 3 , a SAR image IG1 in the targetarea D is generated on the basis of the received signal R0 correspondingto the target area D. In the SAR image IG1, ships S31, S32, and S33 aredetected. In this case, as illustrated in FIG. 4 , the meta-informationMD01, which includes first ship information about the ship and firstoceanographic information about the oceanographic conditions, isacquired from the SAR image IG1. The meta-information MD01 is associatedwith the received signal R0. Note that the item “ship ID” in the firstship information and the second ship information described later isarbitrary information used to identify a ship in the meta-informationMD01.

In addition, based on the AIS information in the target area D obtainedwhen the received signal R0 corresponding to the target area D isacquired, the meta-information MD02 is acquired as illustrated in FIG. 5. The meta-information MD02 includes the second ship information about aship and the second oceanographic information about oceanographicconditions. The second ship information and the second oceanographicinformation have different items from those of the first shipinformation and the second oceanographic information. Themeta-information MD02 is associated with the received signal R0.

In the case of being based on the AIS information, the meta-informationMD02 indicates that the ship S34 is present in the target area D inaddition to the ships S31 to S33, as illustrated in the correct imageIG2 in FIG. 3 .

The meta-information MD0, which is associated with the received signalR0 as the learning data LD, is prepared based on the meta-informationMD1 and the meta-information MD2. The meta-information MD0 includes thethird ship information on the basis of the first ship information so asto have the same items as those of the first ship information withrespect to the ship. Meta-information MD0 also includes the thirdoceanographic information on the basis of the second oceanographicinformation so as to have the same items as those of the secondoceanographic information with respect to the oceanographic conditions.Thus, the meta-information MD0 used for generating a model is able to begenerated by combining the meta-information MD01 based on the SAR imageand the meta-information MD02 based on information from other devices.In addition, the meta-information MD0 may directly be either of themeta-information MD01 and the meta-information MD02.

The learning model 1041 is learned by using the learning data LDprepared as described above, for example, by a general machine learningmethod such as a method using a neural network. The learning model 1041may be composed of a single learning model or a combination of multiplelearning models.

Referring to FIGS. 7 to 9 , the processing by the flying object 100 isdescribed.

In step S701 of FIG. 7 , the radar device 102 irradiates the target areaD with the electromagnetic wave EM1. The timing of the irradiation maybe controlled by the observation device 200 or specified in advance bythe flying object 100.

In step S702, the signal acquisition unit 1051 acquires the receivedsignal R1 based on the reflected electromagnetic wave EM2 detected bythe radar device 102 from the radar device 102.

In step S703, the estimation unit 1052 inputs the received signal R1 tothe learning model 1041. In addition to the received signal R1, theestimation unit 1052 may input additional information obtained when thereceived signal R1 is acquired into the learning model 1041.

In step S704, the estimation unit 1052 acquires the meta-information MD1corresponding to the received signal R1 from the learning model 1041.

In step S705, the signal output unit 1053 outputs an output signal basedon the meta-information MD1 to the observation device 200. The outputsignal based on the meta-information MD1 is a signal that conveys all ora part of the meta-information MD1. Alternatively, the output signal maybe a signal that conveys information of the result of informationprocessing to the meta-information.

Referring to FIG. 8 , the estimation of meta-information by the learningmodel 1041 is described. In this specification, description will be madeon a case of using the learning model 1041 learned with the learningdata LD having the correspondence as illustrated in FIG. 6 .

The received signal R1 is information that may be converted into SARdata indicating the state illustrated in the SAR image IG3. Themeta-information generated based on the SAR image IG3 includesinformation on ships S81 to S83. On the other hand, the actual situationis assumed to be the one illustrated in the correct image IG4. In otherwords, the information about the ship S84 is missing due to theconversion of the received signal R1 into SAR data.

In this case, the signal processor 103 is able to input the receivedsignal R1 to the learning model 1041 to acquire the meta-information MD1as illustrated in FIG. 9 . The meta-information MD1 contains informationindicating four ships. In other words, the signal processor 103 is ableto detect meta-information that cannot be detected from the SAR data.

By using the received signal and the learning model 1041, themeta-information is able to be estimated without being affected by theconversion to the SAR data. Although not illustrated, this enablesappropriate estimation of meta-information, for example, even in thecase of false detection, such as detection of a false image of an objectthat should not be detected in the SAR image.

Referring to FIGS. 10 and 11 , the detection of changes inmeta-information by the flying object 100 is described.

In FIG. 10 , description is made on the detection of changes inmeta-information about buildings. At a certain time T1 (time 1),buildings are placed as illustrated in the placement image IG5. Atanother time T2 (time 2), buildings are placed in the same area asillustrated in the placement image IG6. The placement images IG5 and IG6are, for example, optical or SAR images of the buildings observed fromabove from the air or outer space. The arrangement images IG5 and IG6have predetermined areas of A01 to A04. As illustrated in the placementimage IG6, a new building NB is constructed in the area A03 at time T2.

The following describes an example where the above changes are detectedby using the learning model 1041. It is assumed here that the learningmodel 1041 has learned to output building information, which is thenumber of buildings in each area, as meta-information in response to aninput of a received signal.

FIG. 11 illustrates a flowchart of the processing by the flying object100.

In step S1101, the radar device 102 irradiates the target area with anelectromagnetic wave at time T1.

In step S1102, the signal acquisition unit 1051 acquires a receivedsignal R3 based on the reflected electromagnetic wave detected by theradar device 102 from the radar device 102. The received signal R3 maybe stored in the storage unit 104.

In step S1103, the estimation unit 1052 inputs the received signal R3 tothe learning model 1041.

In step S1104, the estimation unit 1052 acquires meta-information MD3corresponding to the received signal R3 from the learning model 1041.The meta-information MD3 may be stored in the storage unit 104.

In step S1105, the radar device 102 irradiates the target area with anelectromagnetic wave at time T2.

In step S1106, the signal acquisition unit 1051 acquires the receivedsignal R4 based on the reflected electromagnetic wave detected by theradar device 102 from the radar device 102.

In step S1107, the estimation unit 1052 inputs the received signal R4 tothe learning model 1041.

In step S1108, the estimation unit 1052 acquires meta-information MD4corresponding to the received signal R4 from the learning model 1041.

In step S1109, the change determination unit 1054 determines the changein the target area on the basis of the meta-information MD3 and themeta-information MD4. In the case of FIG. 10 , the meta-information MD04indicates that the number of buildings in the area A03 has increased tofour. Therefore, the change determination unit 1054 determines that achange has occurred in the number of buildings.

In step S1110, the change information output unit 1055 determineswhether the change satisfies a predetermined condition. Here, thepredetermined condition is a result of determination of whether or notthere is a change or a condition related to the specific content of thechange. For example, in the example illustrated in FIG. 10 , thecondition may be that there is a change in a certain area or that achange in which the number of buildings increases or decreases occurs inone of the areas, as the specific content of the change.

In this specification, it is assumed that the condition is that a changein which the number of buildings increases occurs in one of the areas.In this case, the change information output unit 1055 determines thatthe change satisfies the predetermined condition.

If it is determined that the change satisfies the predeterminedcondition, in step S1111, the change information output unit 1055outputs the information indicating the change, as an output signal, tothe observation device 200. Unless the predetermined condition issatisfied, the processing is terminated. The output signal is, forexample, a signal for conveying the meta-information MD3 and MD4.Alternatively, the output signal is a signal for extractingmeta-information about the area A03 where a change has occurred andconveying the extracted meta-information. Alternatively, the outputsignal may be a signal for conveying information about the coordinatesindicating a part where the change has occurred in more details, out ofthe area A03 that has been determined to have a change.

The flying object 100 is able to detect a change in the target area onthe basis of meta-information with good precision or good accuracy basedon the received signal. Therefore, the precision or accuracy of changedetection also increases.

The flying object 100 determines a change and transmits themeta-information itself or the information indicating the range of thechange to the observation device 200 only for the area where the changehas occurred, thereby reducing the amount of communication to theobservation device 200. This allows the flying object 100 to reduce thepower consumption. This is an advantage for the flying object 100, whichis limited in available power due to the environment of the space abovethe earth.

FIG. 12 schematically illustrates the learning and estimation of thelearning model 1041A. The learning model 1041A includes, as the learningdata LD, the SAR data (the first generated signal) generated on thebasis of the received signal R0 and additional information, in additionto the received signal R0 and the meta-information MD0.

By preparing the learning data LD in this way, the learning model 1041Ais able to estimate the meta-information MD1 using the received signalR1, the SAR data generated based on the received signal R1 (the secondgenerated signal), and the additional information corresponding to thereceived signal R1 as input.

For example, as additional information, the weather of the target areais able to be used as information indicating the environment in thetarget area when the received signal R0 is acquired. When estimating themeta-information MD1 using the learning model 1041A, informationindicating the environment in the target area when the received signalR1 is acquired may be acquired from other devices and be included intothe input of the learning model 1041A. This enables estimation of themeta-information MD1 with higher precision or higher accuracy.

FIG. 13 illustrates a block diagram of the observation system 10A asanother embodiment. As illustrated in FIG. 13 , the signal processor 103may also be provided so as to be contained in an observation device200A. The control unit 1301 of a flying object 100A transmits thereceived signals acquired by the radar device 102 to the observationdevice 200A. The signal processor 103 of the observation device 200A maybe configured to perform the above processing.

The embodiments described above are intended to facilitate understandingof the present invention and are not intended to be construed aslimiting the present invention. The elements of the embodiments andtheir conditions are not limited to those illustrated in the examples,but may be changed as necessary. Additionally, it is also possible topartially replace or combine different configurations.

REFERENCE SIGNS LIST

-   -   10 observation system    -   100 flying object    -   101 communication antenna    -   102 radar device    -   103 signal processor    -   104 storage unit    -   1041 learning model    -   105 control unit    -   1051 signal acquisition unit    -   1052 estimation unit    -   1053 signal output unit    -   1054 change determination unit    -   1055 change information output unit    -   200 observation device    -   201 communication unit    -   202 signal processing unit

1-10. (canceled)
 11. A signal processor, comprising: a storage unit thatstores a learning model, wherein: the learning model is learned by usingteaching data with a first synthetic aperture radar received signal asinput, the first synthetic aperture radar received signal being based ona reflected electromagnetic wave that is an electromagnetic wave emittedto a first target area and then reflected, and with firstmeta-information as output, the first meta-information corresponding tothe first synthetic aperture radar received signal and having apredetermined item; and the learning model outputs secondmeta-information having the predetermined item in response to a secondsynthetic aperture radar input, the second synthetic aperture radarreceived signal based on a reflected electromagnetic wave that is anelectromagnetic wave emitted to a second target area and then reflected;a signal acquisition unit that acquires the second synthetic apertureradar received signal; an estimation unit that inputs the secondsynthetic aperture radar received signal at time 1 and the secondsynthetic aperture radar received signal at time 2 to the learning modeland then estimates the second meta-information at the time 1corresponding to the second synthetic aperture radar received signal atthe time 1 and the second meta-information at the time 2 correspondingto the second synthetic aperture radar received signal at the time 2;and a change determination unit that determines a change in the secondtarget area on the basis of the second meta-information at the time 1and the second meta-information at the time
 2. 12. The signal processoraccording to claim 11, wherein: the learning model is learned by usingteaching data with the first synthetic aperture radar received signaland a first generated signal generated based on the first syntheticaperture radar received signal as input and with the firstmeta-information as output; and the learning model outputs the secondmeta-information in response to the second synthetic aperture radarreceived signal and a second generated signal generated based on thesecond synthetic aperture radar received signal.
 13. The signalprocessor according to claim 11, wherein: the learning model is learnedby using teaching data with the first synthetic aperture received signaland information indicating an environment in the first target area asinput and with the first meta-information as output; and the learningmodel outputs the second meta-information in response to the secondreceived signal and information indicating an environment in the secondtarget area as input.
 14. The signal processor according to claim 11,wherein: the learning model is learned by using the teaching data withthe first meta-information containing the information indicating theenvironment in the first target area as output; and the learning modeloutputs the second meta-information containing the informationindicating the environment in the second target area.
 15. The signalprocessor according to claim 11, further comprising a change informationoutput unit that outputs change information indicating the change in thecase where the determined change satisfies a predetermined condition.16. A flying object comprising the signal processor according to claim11, the signal processor further comprising a signal output unit thatoutputs an output signal based on the second meta-information to theoutside.
 17. A non-transitory computer-readable medium storing programcausing a computer to perform: a signal acquisition process foracquiring a second synthetic aperture radar received signal that isinput to the storage unit that stores a learning model, wherein: thelearning model is learned by using teaching data with a first syntheticaperture radar received signal as input, the first received syntheticaperture radar signal being based on a reflected electromagnetic wavethat is an electromagnetic wave emitted to a first target area and thenreflected, and with first meta-information as output, the firstmeta-information corresponding to the first synthetic aperture radarreceived signal and having a predetermined item; and the learning modeloutputs second meta-information having the predetermined item inresponse to a second synthetic aperture radar received signal as input,the second synthetic aperture received signal based on a reflectedelectromagnetic wave that is an electromagnetic wave emitted to a secondtarget area and then reflected; an estimation process for inputting thesecond synthetic aperture received signal at time 1 and the secondsynthetic aperture received signal at time 2 to the learning model andthen estimates the second meta-information at the time 1 correspondingto the second synthetic aperture received signal at the time 1 and thesecond meta-information at the time 2 corresponding to the secondsynthetic aperture received signal at the time; and a changedetermination process for determining a change in the second target areaon the basis of the second meta-information at the time 1 and the secondmeta-information at the time
 2. 18. The non-transitory computer-readablemedium storing program according to claim 17, causing the computer tofurther perform a signal output process for outputting the output signalbased on the second meta-information to the outside.